System and method for a data driven meta-auction mechanism for sponsored search

Abstract

Apparatuses, methods, and systems directed to deriving optimal parameters of a learning algorithm to maximize an objective function of online keyword auctions for bidded terms. Some embodiments of the invention simulate online keyword auctions based on historical data for the bidded terms, wherein the parameters of the simulated auctions such as market reserve prices of the bidded terms are determined by an adaptive learning algorithm. The values of the parameters of the learning algorithm are optimized by a stochastic optimization method to maximize an objective function for the auctions of the bidded terms.

What is claimed is:  
     
       1. A method, implemented on a machine having at least one processor, storage, and a communication platform connected to a network, comprising the steps of:
 receiving, via the communication platform, one or more bids for a bidded term comprising one or more keywords from one or more bidders in an auction, wherein said auction occurs in an online auction platform; 
 retrieving data from a data store operatively couple to the online auction platform wherein the data comprises values of one or more auction parameters related to past auctions of the bidded term; 
 computing, by the at least one processor, updated values of the one or more auction parameters based on the retrieved data using an adaptive learning algorithm; 
 acquiring a winning bid based on the computed auction parameters; and 
 storing the bidded term, the one or more bids, the updated values of the auction parameters, and the: winning bid in the data store. 
 
     
     
       2. The method of  claim 1 , wherein the auction parameters comprise a market reserve price for the bidded term and the acquiring step comprising:
 excluding bids which are below the market reserve price; 
 selecting a winning bid from the remaining bids; and 
 acquiring additional bids if all bids are excluded in the exclusion step, the step of acquiring additional bids comprising:
 outputting the updated values of the auction parameters for display; and 
 receiving zero or more additional bids for the bidded term until the auction is completed. 
 
 
     
     
       3. The method of  claim 1 , wherein the one or more auction parameters comprise market reserve prices of the bidded term and durations of the auctions. 
     
     
       4. The method of  claim 1 , wherein the adaptive learning algorithm comprises one or more learning parameters optimized by a stochastic optimization method in simulated auctions using historical data. 
     
     
       5. The method of  claim 1 , wherein the adaptive learning algorithm comprises the softmax action selection process. 
     
     
       6. The method of  claim 4 , wherein the stochastic optimization method comprises the simultaneous perturbation stochastic approximation process. 
     
     
       7. An apparatus, comprising:
 a memory; 
 one or more processors; and 
 logic encoded in one or more computer readable medium, wherein the logic when executed is operable to use the one or more processors to;
 receive one or more bids for a bidded term comprising one or more keywords from one or more bidders in an auction, said auction occurs in an online auction platform; 
 retrieve data from a data store operatively coupled to the online auction platform wherein the data comprises values of one or more auction parameters and one or more bids related to past auctions of the bidded term; 
 compute updated values of the one or more auction parameters based on the retrieved data using an adaptive learning algorithm; 
 output the updated values of the auction parameters for display; 
 acquire zero or more additional bids for the bidded term until the auction is completed; and 
 store the bidded term, the one or more bids, the updated values of the auction parameters, and the acquired additional bids in the data store. 
 
 
     
     
       8. The apparatus of  claim 7 , wherein the one or more auction parameters comprise market reserve prices of the bidded term and durations of the auctions. 
     
     
       9. The apparatus of  claim 7 , wherein the adaptive learning algorithm comprises one or more learning parameters optimized by a stochastic optimization method in simulated auctions using historical data. 
     
     
       10. The apparatus of  claim 7 , wherein the adaptive learning algorithm comprises the softmax action selection process. 
     
     
       11. The apparatus of  claim 9 , wherein the stochastic optimization method comprises the simultaneous perturbation stochastic approximation process.